The invention relates generally to magnetic resonance imaging (MRI) and, more particularly, to MR image processing, including a method and apparatus to reduce the scan times associated with MR imaging.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well-known reconstruction techniques.
Typically, new MR imaging systems are a significant investment for an entity, including the MR imaging system itself as well as the cost of constructing a specialized suite to house the system. Due the high cost of purchasing and maintaining an MR imaging system, there are usually a limited number of such systems in operation at a given location. The high ratio of patients requiring MR scans to the number of available MRI systems makes patient throughput a critical feature of the MRI system. One common way to increase patient throughput is by decreasing the scan time for each patient.
Generating an MR image involves taking a series of repeated measurements. Generally, each series of measurements corresponds to a 3D slice of the area being imaged. One magnetic gradient, known as the slice select gradient, is used to determine the width of the slice. A second magnetic gradient, the phase encoding gradient, is used to spatially vary the phase of the emitted signal along the axis of the phase encoding gradient, while a third frequency encoding gradient is used to spatially vary the frequency of the emitted signal along the axis of the frequency encoding gradient. The total time required to complete a scan of the 3D slice may be calculated using the following expression:
Total imaging time=TR×N×NSA, 
where TR is the time between repeated measurements (or the time between phase encoding steps), N is the number of phase encoding steps, and NSA is the number of signal averages.
As an example, an imaging sequence may require 128 phase encoding steps. For each phase encoding step, there may be 256 frequency encoding steps during which measurements are taken. The time required to complete all 256 frequency encoding steps, together with the number of signal averages, determines the length of time between repeated measurements, or the time between phase encoding steps (TR). Moreover, the measurements taken during each particular phase encoding step may be repeated to improve the signal-to-noise ratio. The number of repetitions per phase encoding step is referred to as the number of signal averages (NSA).
In MR imaging, the scan time can be reduced by using partial Fourier acquisition to omit some of the phase encoding steps on one side of k-space. Data acquired using this technique can be reconstructed using a variation of Hermitian conjugate symmetry or by zero-filling. Zero-filling generally gives poor image quality compared to Hermitian conjugate symmetry (also known as homodyne reconstruction), except in some limited situations.
A newer method for reducing scan time is compressed sensing which also allows for fewer phase encoding steps than would be required for Nyquist sampling. Data acquired using compressed sensing is reconstructed using an L1-norm constraint to recover missing information. Until now, efforts to combine compressed sensing with partial Fourier acquisition have yielded less than optimal results due to the fact that zero filling, the only known algorithm for reconstructing the data, generally gives poor image quality.
It would therefore be desirable to have a system and method capable of combining compressed sensing and partial Fourier acquisition that can produce high-quality reconstructed MR images.